Fluidized bed combustion (FBC) is a combustion technology used in power plants, primarily to burn solid fuels. FBC power plants are more flexible than conventional plants in that they can be fired on coal, coal waste or biomass, among other fuels. In general, FBC power plants evolved from efforts to find a combustion process able to control pollutant emissions without external emission controls (such as scrubbers). Although FBC power plants have lower pollutant emissions than conventional combustion plants, ongoing efforts continually strive to reduce pollutant emissions to even lower levels.
Chemical looping (CL) is another combustion technology which can also be utilized in electrical power generation plants which burn fuels such as coal, coal waste, biomass, and other opportunity fuels. The CL process can be implemented in existing or new power plants, and provides promising improvements in terms of reduced plant size, reduced emissions, and increased plant operational efficiency, among other benefits.
A typical CL system utilizes a high temperature process, whereby solids such as calcium- or metal-based compounds, for example, are “looped” between a first reactor, called an oxidizer, and a second reactor, called a reducer. In the oxidizer, oxygen from air injected into the oxidizer is captured by the solids in an oxidation reaction. The captured oxygen is then carried by the oxidized solids to the reducer to be used for combustion and/or gasification of a fuel such as coal, for example. After a reduction reaction in the reducer, the solids, no longer having the captured oxygen, are returned to the oxidizer to be oxidized again, and the cycle repeats.
The CL process is more complicated than processes of other plants such as conventional circulating fluidized bed (CFB) plants, for example. In particular, control of circulating solids in the CL process requires multi-loop interactive flow and inventory controls which are not required in traditional plants. As a result, traditional plant controls applied to the CL process necessarily result in separate control loops for each CL loop. However, using separate control loops for each CL loop is inefficient and does not optimize performance of the CL process, since accurate control requires coordinated control of parameters between individual loops. Interactions between variables for each loop of the CL process have to be taken into account to optimize overall CL process performance. Solids flow between loops, for example, is particularly difficult to regulate, due a large number of nonlinear, interrelated variables associated with the solids flow. More specifically, oscillation coupling between loops of a multiple-loop CL-based plant, for example, disrupts flow and makes solids inventory regulation thereof difficult. Also, crossover flows interact with main, e.g., recirculation, flows of opposite loops, thereby complicating overall regulation of solids transport with each respective loop.
Control and optimization tools which have been developed thus far are focused on controlling and optimizing conventional combustion power plants. As a result, these tools have been focused on solving very specific, localized problems rather than global control and optimization of complex plant operations.
Control systems using conventional process controls based on fuzzy set theory (fuzzy logic) have been developed to help overcome some the problems described above. Fuzzy set theory is based on rule-based decision making which emulates a “rule of thumb” reasoning process similar to that of human thought and decision making. However, conventional fuzzy set theory control systems are limited in the number rules which can be memorized, since an excessive number of rules overburdens the fuzzy logic decision making process, effectively obviating the advantages of using fuzzy logic. Thus, as power plant designs evolve and processes thereof become more complex, such as with CL-based power plants described above and, specifically, with multi-loop CL-based power plants, the number of variables involved increases dramatically. As a result, a number of required rules becomes unacceptable, and conventional fuzzy set theory control systems are thereby unable to optimally or efficiently control certain processes, such as solids transport, for example, of a CL-based power plant.
Accordingly, it is desired to develop a control and optimization system for solids transport, for example, in a CFB system or a CL system which overcomes the shortfalls described above.